A review of medical imaging informatics

Automatic detection of squamous cell carcinoma metastasis in esophageal lymph nodes using semantic segmentation

Esophageal squamous cell carcinoma (ESCC) is more prevalent than esophageal adenocarcinoma in Asia, especially in China, where more than half of ESCC cases occur worldwide. Many studies have reported that the automatic detection of lymph node metastasis using semantic segmentation shows good performance in breast cancer and other adenocarcinomas. However, the detection of squamous cell carcinoma metastasis in hematoxylin-eosin (H&E)-stained slides has never been reported. We collected a training set of 110 esophageal lymph node slides with metastasis and 132 lymph node slides without metastasis. An iPad-based annotation system was used to draw the contours of the cancer metastasis region. A DeepLab v3 model was trained to achieve the best fit with the training data. The learned model could estimate the probability of metastasis. To evaluate the effectiveness of the detection model of learned metastasis, we used another large cohort of clinical H&E-stained esophageal lymph node slides containing 795 esophageal lymph nodes from 154 esophageal cancer patients. The basic authenticity label for each slide was confirmed by experienced pathologists. After filtering isolated noise in the prediction, we obtained an accuracy of 94%. Furthermore, we applied the learned model to throat and lung lymph node squamous cell carcinoma metastases and achieved the following promising results: an accuracy of 96.7% in throat cancer and an accuracy of 90% in lung cancer. In this work, we organized an annotated dataset of H&E-stained esophageal lymph node and trained a deep neural network to detect lymph node metastasis in H&E-stained slides of squamous cell carcinoma automatically. Moreover, it is possible to use this model to detect lymph nodes metastasis in squamous cell carcinoma from other organs. This study directly demonstrates the potential for determining the localization of squamous cell carcinoma metastases in lymph node and assisting in pathological diagnosis.

Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association

In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber-security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Detection of blur artifacts in histopathological whole-slide images of endomyocardial biopsies

Histopathological whole-slide images (WSIs) have emerged as an objective and quantitative means for image-based disease diagnosis. However, WSIs may contain acquisition artifacts that affect downstream image feature extraction and quantitative disease diagnosis. We develop a method for detecting blur artifacts in WSIs using distributions of local blur metrics. As features, these distributions enable accurate classification of WSI regions as sharp or blurry. We evaluate our method using over 1000 portions of an endomyocardial biopsy (EMB) WSI. Results indicate that local blur metrics accurately detect blurry image regions.

Generic integration of content-based image retrieval in computer-aided diagnosis

Content-based image retrieval (CBIR) offers approved benefits for computer-aided diagnosis (CAD), but is still not well established in radiological routine yet. An essential factor is the integration gap between CBIR systems and clinical information systems. The international initiative Integrating the Healthcare Enterprise (IHE) aims at improving interoperability of medical computer systems. We took into account deficiencies in IHE compliance of current picture archiving and communication systems (PACS), and developed an intermediate integration scheme based on the IHE post-processing workflow integration profile (PWF) adapted to CBIR in CAD. The Image Retrieval in Medical Applications (IRMA) framework was used to apply our integration scheme exemplarily, resulting in the application called IRMAcon. The novel IRMAcon scheme provides a generic, convenient and reliable integration of CBIR systems into clinical systems and workflows. Based on the IHE PWF and designed to grow at a pace with the IHE compliance of the particular PACS, it provides sustainability and fosters CBIR in CAD.

Mammogram retrieval through machine learning within BI-RADS standards

A content-based mammogram retrieval system can support usual comparisons made on images by physicians, answering similarity queries over images stored in the database. The importance of searching for similar mammograms lies in the fact that physicians usually try to recall similar cases by seeking images that are pathologically similar to a given image. This paper presents a content-based mammogram retrieval system, which employs a query example to search for similar mammograms in the database. In this system the mammographic lesions are interpreted based on their medical characteristics specified in the Breast Imaging Reporting and Data System (BI-RADS) standards. A hierarchical similarity measurement scheme based on a distance weighting function is proposed to model user's perception and maximizes the effectiveness of each feature in a mammographic descriptor. A machine learning approach based on support vector machines and user's relevance feedback is also proposed to analyze the user's information need in order to retrieve target images more accurately. Experimental results demonstrate that the proposed machine learning approach with Radial Basis Function (RBF) kernel function achieves the best performance among all tested ones. Furthermore, the results also show that the proposed learning approach can improve retrieval performance when applied to retrieve mammograms with similar mass and calcification lesions, respectively.

Integrating imaging informatics into the radiology residency curriculum: rationale and example curriculum

Imaging informatics, as part of the wider emerging discipline of medical informatics, remains poorly defined. However, many educators agree that formalized and flexible training in the collection, display, manipulation, storage, retrieval, and communication of imaging data, as well as the integration of these data into larger databases, should be introduced into the period of radiology residency training. The authors review the importance of such training to those individuals now preparing for clinical practice and research. They describe a sample imaging informatics curriculum that can be incorporated into a 4-year radiology residency program and the significance of such training in establishing a new subdiscipline focusing on imaging information technologies.

Content-based access to oral and maxillofacial radiographs

OBJECTIVES

Content-based access (CBA) to medical image archives, i.e. data retrieval by means of image-based numerical features computed automatically, has capabilities to improve diagnostics, research and education. In this study, the applicability of CBA methods in dentomaxillofacial radiology is evaluated.

METHODS

Recent research has discovered numerical features that were successfully applied for an automatic categorization of radiographs. In our experiments, oral and maxillofacial radiographs were obtained from the day-to-day routine of a university hospital and labelled by an experienced dental radiologist regarding the technique and direction of imaging, as well as the displayed anatomy and biosystem. In total, 2000 radiographs of 71 classes with at least 10 samples per class were analysed. A combination of co-occurrence-based texture features and correlation-based similarity measures was used in leaving-one-out experiments for automatic classification. The impact of automatic detection and separation of multi-field images and automatic separability of biosystems were analysed.

RESULTS

Automatic categorization yielded error rates of 23.20%, 7.95% and 4.40% with respect to a correct match within the first, fifth and tenth best returns. These figures improved to 23.05%, 7.00%, 4.20%, and 20.05%, 5.65% and 3.25% if automatic decomposition was applied and the classifier was optimized to the dentomaxillofacial imagery, respectively. The dentulous and implant systems were difficult to distinguish. Experiments on non-dental radiographs (10,000 images of 57 classes) yielded 12.6%, 5.6% and 3.6%.

CONCLUSION

Using the same numerical features as in medical radiology, oral and maxillofacial radiographs can be reliably indexed by global texture features for CBA and data mining.

Bioinformatics and its applications in plant biology

Bioinformatics plays an essential role in today's plant science. As the amount of data grows exponentially, there is a parallel growth in the demand for tools and methods in data management, visualization, integration, analysis, modeling, and prediction. At the same time, many researchers in biology are unfamiliar with available bioinformatics methods, tools, and databases, which could lead to missed opportunities or misinterpretation of the information. In this review, we describe some of the key concepts, methods, software packages, and databases used in bioinformatics, with an emphasis on those relevant to plant science. We also cover some fundamental issues related to biological sequence analyses, transcriptome analyses, computational proteomics, computational metabolomics, bio-ontologies, and biological databases. Finally, we explore a few emerging research topics in bioinformatics.

MIPortal: A High Capacity Server for Molecular Imaging Research

The introduction of novel molecular tools in research and clinical medicine has created a need for more refined information management systems. This article describes the design and implementation of such a new information platform: the Molecular Imaging Portal (MIPortal). The platform was created to organize, archive, and rapidly retrieve large datasets using Web-based browsers as access points. The system has been implemented in a heterogeneous, academic research environment serving Macintosh, Unix, and Microsoft Windows clients and has been shown to be extraordinarily robust and versatile. In addition, it has served as a useful tool for clinical trials and collaborative multi-institutional small-animal imaging research.

Use of a human visual system model to predict observer performance with CRT vs LCD display of images

This Project evaluated a human visual system model (JNDmetrix) based on just noticeable difference (JND) and frequency-channel vision-modeling principles to assess whether a Cathode ray tube (CRT) or a liquid crystal display (LCD) monochrome display monitor would yield better observer performance in radiographic interpretation. Key physical characteristics, such as veiling glare and modulation transfer function (MTF) of the CRT and LCD were measured. Regions of interest from mammographic images with masses of different contrast levels were shown once on each display to six radiologists using a counterbalanced presentation order. The images were analyzed using the JNDmetrix model. Performance as measured by receiver operating characteristic (ROC) analysis was significantly better overall on the LCD display (P = 0.0120). The JNDmetrix model predicted the result (P = 0.0046) and correlation between human and computer observers was high (r (2) (quadratic) = 0.997). The results suggest that observer performance with LCD displays is superior to CRT viewing, at least for on-axis viewing.

The cell-centered database: a database for multiscale structural and protein localization data from light and electron microscopy

The creation of structured shared data repositories for molecular data in the form of web-accessible databases like GenBank has been a driving force behind the genomic revolution. These resources serve not only to organize and manage molecular data being created by researchers around the globe, but also provide the starting point for data mining operations to uncover interesting information present in the large amount of sequence and structural data. To realize the full impact of the genomic and proteomic efforts of the last decade, similar resources are needed for structural and biochemical complexity in biological systems beyond the molecular level, where proteins and macromolecular complexes are situated within their cellular and tissue environments. In this review, we discuss our efforts in the development of neuroinformatics resources for managing and mining cell level imaging data derived from light and electron microscopy. We describe the main features of our web-accessible database, the Cell Centered Database (CCDB; http://ncmir.ucsd.edu/CCDB/), designed for structural and protein localization information at scales ranging from large expanses of tissue to cellular microdomains with their associated macromolecular constituents. The CCDB was created to make 3D microscopic imaging data available to the scientific community and to serve as a resource for investigating structural and macromolecular complexity of cells and tissues, particularly in the rodent nervous system.